Spurious rejections of the standard Dickey-Fuller (DF) test caused by a single variance break have been reported and some solutions to correct the problem have been proposed in the literature. Kim et al. (2002) put forward a correctly-sized unit root test robust to a single variance break, called the KLN test. However, there can be more than one break in variance in time series data as documented in Zhou and Perron (2008), so allowing only one break can be too restrictive. In this paper, we show that multiple breaks in variance can generate spurious rejections not only by the standard DF test but also by the KLN test. We then propose a bootstrap-based unit root test that is correctly-sized in the presence of multiple breaks in variance. Simulation experiments demonstrate that the proposed test performs well regardless of the number of breaks and the location of the breaks in innovation variance.
Bibliographical noteFunding Information:
We are grateful for the helpful comments from the participants at the seventh Joint Economic Symposium of Five Leading East Asian Universities held at the University of Singapore in January 2013. Tae-Hwan Kim acknowledges that this work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2014S1A5A2A01010546).
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All Science Journal Classification (ASJC) codes
- Economics and Econometrics